6 citations · 19 across the 11 of their papers we have counts for
11 papers
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills
Aidan Curtis, Eric Li, Michael Noseworthy +5
Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies trained in simulation. By randomizing environment prope…
Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
Aidan Curtis, George Matheos, Nishad Gothoskar +4
Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAM…
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes
Nishad Gothoskar, Matin Ghavami, Eric Li +8
Robots cannot yet match humans' ability to rapidly learn the shapes of novel 3D objects and recognize them robustly despite clutter and occlusion. We present Bayes3D, an uncertaint…
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
Guangyao Zhou, Nishad Gothoskar, Lirui Wang +5
The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understandi…
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok +3
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102.11938) prov…
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu +4
Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods either require precise calibratio…
3DP3: 3D Scene Perception via Probabilistic Programming
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg +6
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3…
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George
For an intelligent agent to flexibly and efficiently operate in complex environments, they must be able to reason at multiple levels of temporal, spatial, and conceptual abstractio…
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar +3
Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once,…
Learning a generative model for robot control using visual feedback
Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal +2
We introduce a novel formulation for incorporating visual feedback in controlling robots. We define a generative model from actions to image observations of features on the end-eff…
Learning higher-order sequential structure with cloned HMMs
Antoine Dedieu, Nishad Gothoskar, Scott Swingle +3
Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to rep…